Financial services companies, such as banks, credit card companies, investment companies, and the like, handle numerous types of financial transactions as well as non-monetary or administrative transactions on a daily basis. Examples of financial transactions may include deposits, withdrawals, debit card payments, credit card payments, funds transfers, bill payments and the like. Examples of non-monetary transactions may include opening of new accounts, account closures, address changes, user ID and/or password changes, account type changes, and the like. These various transactions make it possible and convenient for consumers and businesses alike to buy and sell goods and services and to generally carry out commercial as well as non-commercial activities.
Most transactions are legitimate events that are carried out for genuine business or personal reasons. However, a small (but growing) number of transactions are conducted with the intent to launder money, commit theft, perpetrate fraud, and other financial crimes. In a typical fraudulent transaction, an unknown party opens a new bank account and makes several small, but legitimate deposits, all of which clear the bank in due course. Shortly thereafter, however, the party makes a much larger, but fraudulent deposit into the account, and begins almost immediately to withdraw all or a portion of the deposited amount. The unwary bank, not suspecting that a fraudulent transaction is in progress, allows the party to withdraw the funds before the deposit has cleared. Several days later when the bank realizes that a fraud has been perpetrated (i.e., because the deposited funds have not cleared), the perpetrator and withdrawn funds are nowhere to be found.
Several tools are commercially available to help detect and prevent the above and other types of fraudulent activities. These tools are generally referred to as suspicious activity monitors (SAM) and are designed to detect transactions that may be indicative of a financial crime. Basically, these SAM tools take in data concerning a transaction and compare the data to a set of rules or scenarios that are defined based on experience with previous fraudulent activities. The rules and scenarios may include, for example, deposits over a certain amount (e.g., $100,000) into a personal account, remotely accessing an account from locations known for fraudulent activities (e.g., Eastern Europe, Asia), and the like. If the transaction violates one of the rules or fits one of the scenarios, then the SAM tool issues an alert notifying the financial services company that a fraudulent activity may be taking place.
Existing SAM tools, however, lack the ability to handle disparate types of transaction data from multiple different financial services. A single integrated financial services company may provide, for example, banking services, credit card services, investment services, and the like. Each financial service may constitute a separate business unit within the integrated financial services company may have one or more lines of business. Thus, each of the financial services may produce transaction data in its own format, timing, and/or channel that may be different from the other financial services.
Accordingly, what is needed is a way to be able to standardize or otherwise convert to a common format the transaction data generated by the different financial services, and to be able to present this standardized transaction data to the SAM tool over a common channel substantially in real time.